class_docstr: CVScores displays cross-validated scores as a bar chart, with the
  average of the scores plotted as a horizontal line.
  
  Parameters
  ----------
  
  estimator : a scikit-learn estimator
      An object that implements ``fit`` and ``predict``, can be a
      classifier, regressor, or clusterer so long as there is also a valid
      associated scoring metric.
      Note that the object is cloned for each validation.
  
  ax : matplotlib.Axes object, optional
      The axes object to plot the figure on.
  
  cv : int, cross-validation generator or an iterable, optional
      Determines the cross-validation splitting strategy.
      Possible inputs for cv are:
  
      - None, to use the default 3-fold cross-validation,
      - integer, to specify the number of folds.
      - An object to be used as a cross-validation generator.
      - An iterable yielding train/test splits.
  
      See the scikit-learn `cross-validation guide <https://goo.gl/FS3VU6>`_
      for more information on the possible strategies that can be used here.
  
  scoring : string, callable or None, optional, default: None
      A string or scorer callable object / function with signature
      ``scorer(estimator, X, y)``.
  
      See scikit-learn `cross-validation guide <https://goo.gl/FS3VU6>`_
      for more information on the possible metrics that can be used.
  
  color: string
      Specify color for barchart
  
  kwargs : dict
      Keyword arguments that are passed to the base class and may influence
      the visualization as defined in other Visualizers.
  
  Attributes
  -------
  cv_scores_ : ndarray shape (n_splits, )
      The cross-validated scores from each subsection of the data
  
  cv_scores_mean_ : float
      Average cross-validated score across all subsections of the data
  
  
  Examples
  --------
  
  >>> from sklearn import datasets, svm
  >>> iris = datasets.load_iris()
  >>> clf = svm.SVC(kernel='linear', C=1)
  >>> X = iris.data
  >>> y = iris.target
  >>> visualizer = CVScores(estimator=clf, cv=5, scoring='f1_macro')
  >>> visualizer.fit(X,y)
  >>> visualizer.show()
  
  Notes
  -----
  
  This visualizer is a wrapper for
  `sklearn.model_selection.cross_val_score <https://goo.gl/4v7dfL>`_.
  
  Refer to the scikit-learn
  `cross-validation guide <https://goo.gl/FS3VU6>`_
  for more details.
code: def __init__(
      self,
      estimator,
      ax=None,
      cv=None,
      scoring=None,
      color=None,
      **kwargs,
  ):
      super(CVScores, self).__init__(estimator, ax=ax, **kwargs)
  
      self.cv = cv
      self.color = color
      self.scoring = scoring
  
file_path: yellowbrick/yellowbrick/model_selection/cross_validation.py
imports: import numpy
  import matplotlib.ticker
  from yellowbrick.base import ModelVisualizer
  from sklearn.model_selection import cross_val_score
label_desc: Use image node_id 1 to create a new CVScores object from inherited base classes: ModelVisualizer with example: obj = CVScores(estimator, ax, cv, scoring, color)